TrainingPeaks Data

Preliminary tasks

Load Libraries

Load libraries for reading fit data and create maps and plots.

Manual Example (WhiteLiam)

Date: 16 April 2019, cycling

File: fit-test_cycling_2019-04-16_05-29-42.csv.fit

data <- read.fit('fit-test_cycling_2019-04-16_05-29-42.csv.fit')

What can we get from TrainingPeaks?

Lots of metadata from the fit file: * file id * device settings * user properties * sport / sport type * etc.

 [1] "file_id"         "device_settings" "user_profile"    "zones_target"   
 [5] "sport"           "session"         "lap"             "record"         
 [9] "event"           "device_info"     "activity"        "file_creator"   
[13] "hrv"             "unknown"

And more specifics about the activity:

With their corresponding units:

matrix(c(attr(data$record,'names'),attr(data$record,'units')),ncol=2)

      [,1]                         [,2]     
 [1,] "accumulated_power"          "watts"  
 [2,] "altitude"                   "m"      
 [3,] "cadence"                    "rpm"    
 [4,] "combined_pedal_smoothness"  "percent"
 [5,] "distance"                   "m"      
 [6,] "enhanced_altitude"          "m"      
 [7,] "enhanced_speed"             "m/s"    
 [8,] "fractional_cadence"         "rpm"    
 [9,] "heart_rate"                 "bpm"    
[10,] "left_pedal_smoothness"      "percent"
[11,] "left_right_balance"         ""       
[12,] "left_torque_effectiveness"  "percent"
[13,] "position_lat"               "degrees"
[14,] "position_long"              "degrees"
[15,] "power"                      "watts"  
[16,] "right_pedal_smoothness"     "percent"
[17,] "right_torque_effectiveness" "percent"
[18,] "speed"                      "m/s"    
[19,] "temperature"                "C"      
[20,] "timestamp"                  "s"

Basic Plots

Elevation vs Time

pdata <- with(data$record, data.frame(alt = altitude, time = (timestamp-timestamp[1])/60))
 ggplot(pdata, aes(y=alt, x=time)) + geom_line() +
   ggtitle("Elevation vs Time") + xlab("time (minutes)") + ylab("elevation (m)")

Elevation vs Distance

pdata <- with(data$record, data.frame(alt = altitude, time = (distance-distance[1])/1000))
 ggplot(pdata, aes(y=alt, x=time)) + geom_line() +
   ggtitle("Elevation vs Distance") + xlab("Distance (km)") + ylab("elevation (m)")

Summary of all variables (Strava-like)

pdata <- with(data$record, data.frame(alt = altitude, time = (distance-distance[1])/1000,
                                      pow = power, hr=heart_rate, speed=speed, cad=cadence))
pdata <- pdata[-(1:10),]
pdata$grad <- with(data$record, 100 * diff(altitude,lag=10) / diff(distance,lag=10))

 ggplot(pdata, aes(y=alt, x=time)) +
   geom_line(aes(y=alt,colour="Altitude")) +
   geom_line(aes(y=pow*2,colour="Power"),size=0.05) +
   geom_line(aes(y=hr*2,colour="HR"),size=0.1) +
   geom_line(aes(y=speed*3.69*2,colour="Speed"),size=0.1) +
   geom_line(aes(y=cad*2,colour="Cadence"),size=0.05) +
   geom_line(aes(y=grad*10+min(pdata$alt),colour="Gradient"),size=0.2) +
   # geom_abline(intercept=min(pdata$alt), slope=0,color="orange",size=1,lty="longdash") +
   geom_segment(aes(x = 0, xend = max(pdata$time), y = min(pdata$alt), yend = min(pdata$alt)),color="orange",size=0.8) +
   scale_y_continuous(sec.axis = sec_axis(~./2, name = "HR (bpm) / Power (W) / Gradient (%) \n Speed (kph) / Cadence(rpm)"),
                      breaks = seq(0, 1000, by = 200)) +
   scale_x_continuous(breaks= seq(0, 70, by = 10)) +
   ggtitle("Distance vs All") +
   xlab("Distance (km)") +
   ylab("Altitude (m)") +
   scale_colour_manual(values = c("black", "purple","orange","red","blue","green")) +
   labs(y = "Altitude (m)",
                x = "Distance (km)",
                colour = "") +
   theme_minimal() +
   # theme(legend.position = c(0.9, 0.93))
   theme(legend.position = "bottom")

Heart Rate vs Gradient

The steepest the climb, the hardest the heart has to work.

pdata <- data$record[-(1:10),c("heart_rate","timestamp")]
# compute average gradient, as %
 pdata$gradient <- with(data$record, 100 * diff(altitude,lag=10) / diff(distance,lag=10))
 pdata <- subset(pdata, complete.cases(pdata) & abs(gradient) < 7.5 & gradient != 0) # drop outliers
 ggplot(pdata, aes(x=gradient, y=heart_rate)) +
   geom_point(alpha=0.5) + geom_jitter() +
   stat_smooth(method="lm", formula=y ~ poly(x, 2)) +
   ggtitle("Heart rate vs gradient")

With the corresponding correlation:


Call:
lm(formula = heart_rate ~ poly(gradient, 2), data = pdata)

Residuals:
   Min     1Q Median     3Q    Max 
-88.10 -12.44  -1.12  11.48  69.91 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         136.9456     0.2314  591.83   <2e-16 ***
poly(gradient, 2)1 2121.3849    19.6863  107.76   <2e-16 ***
poly(gradient, 2)2  264.0658    19.6863   13.41   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 19.69 on 7235 degrees of freedom
Multiple R-squared:  0.6198,    Adjusted R-squared:  0.6196 
F-statistic:  5896 on 2 and 7235 DF,  p-value: < 2.2e-16

And averages for each gradient step:

pred <- data.frame(gradient = seq(-6,6,2))
pred$heart_rate <- predict(fit, pred)
t(pred)

               [,1]     [,2]    [,3]     [,4]     [,5]     [,6]     [,7]
gradient   -6.00000  -4.0000  -2.000   0.0000   2.0000   4.0000   6.0000
heart_rate 96.57316 105.1922 115.464 127.3885 140.9658 156.1958 173.0786

Activity Maps

Convert time to minutes and add direction (Outbound vs Return):

 # points <- subset(data$record, complete.cases(data$record))
points <- data$record
points$time_min  <- with(points, timestamp - timestamp[1])/60 # minutes of riding

# from diagram above, we turned around at the 90 minutes mark
# points[which(points$altitude == max(points$altitude)),]
points$direction <- with(points, factor(ifelse(time_min < 90, 'Outbound', 'Return')))

 # library(leaflet)
 # leaflet(points[points$direction == 'Outbound',]) %>% addTiles() %>% addPolylines(~position_long,~position_lat)
 # leaflet(points[points$direction == 'Return',]) %>% addTiles() %>% addPolylines(~position_long,~position_lat)

Map with HR

The darker the colour, the higher the HR is:

newcols <- rev(cet_pal(min(dim(points)[1],256),name="l3"))
points <- points %>% mutate(quantile=ntile(heart_rate,256))
newcols.quantile <- newcols[points$quantile]
points1 <- points %>%
 mutate(nextLat = lead(position_lat),
        nextLng = lead(position_long),
        color = newcols.quantile
        )
gradient_map <- leaflet() %>%
 addTiles()
# points1 <- points1[points1$direction == 'Outbound',]
for (i in 1:nrow(points1)) {
 gradient_map <- addPolylines(map = gradient_map,
                              data = points1,
                              lng = as.numeric(points1[i, c('position_long', 'nextLng')]),
                              lat = as.numeric(points1[i, c('position_lat', 'nextLat')]),
                              color = as.character(points1[i, c('color')])
 )
}
gradient_map

Map with HR and profile

And we can overlap different measurements to the same plot. The darker the colour, the higher the HR and the higher the altitude (for the new line):

newcols <- rev(cet_pal(min(dim(points)[1],256),name="l3"))
newcols_alt <- rev(cet_pal(min(dim(points)[1],256),name="l7"))
points <- points %>% mutate(quantile=ntile(heart_rate,256))
points <- points %>% mutate(quantile_alt=ntile(altitude,256))
newcols.quantile <- newcols[points$quantile]
points1 <- points %>%
 mutate(nextLat = lead(position_lat),
        nextLng = lead(position_long),
        color = newcols.quantile,
        color_alt = newcols_alt[points$quantile_alt]
        )
gradient_map <- leaflet() %>% addTiles()
# points1 <- points1[points1$direction == 'Outbound',]
for (i in 1:nrow(points1)) {
# for (i in 1:500) {
 gradient_map <- addPolylines(map = gradient_map,
                              data = points1,
                              lng = as.numeric(points1[i, c('position_long', 'nextLng')]),
                              lat = as.numeric(points1[i, c('position_lat', 'nextLat')]),
                              color = as.character(points1[i, c('color')])
 )
}

for (i in 1:which.max(points1$quantile_alt)) {
# for (i in 1:500) {
 gradient_map <- addPolylines(map = gradient_map,
                              data = points1,
                              lng = as.numeric(points1[i, c('position_long', 'nextLng')])-0.005,
                              lat = as.numeric(points1[i, c('position_lat', 'nextLat')])-0.005,
                              color = as.character(points1[i, c('color_alt')])
 )
}

gradient_map

Map of a different activity

Function to create map:

create_map_fit <- function (fit_data,alt){
  points <- fit_data$record
  newcols <- rev(cet_pal(min(dim(points)[1],256),name="l3"))
  newcols_alt <- rev(cet_pal(min(dim(points)[1],256),name="l7"))
  points <- points %>% mutate(quantile=ntile(heart_rate,256))
  if (alt == TRUE) {
    points <- points %>% mutate(quantile_alt=ntile(altitude,256))
  }
  if (alt == TRUE) {
    points1 <- points %>% mutate(nextLat = lead(position_lat),
                               nextLng = lead(position_long),
                               color = newcols[points$quantile],
                               color_alt = newcols_alt[points$quantile_alt])
  } else {
    points1 <- points %>% mutate(nextLat = lead(position_lat),
                               nextLng = lead(position_long),
                               color = newcols[points$quantile])
  }
  
  #prepare map
  gradient_map <- leaflet() %>% addTiles()
  for (i in 1:nrow(points1)) {
   gradient_map <- addPolylines(map = gradient_map,
                                data = points1,
                                lng = as.numeric(points1[i, c('position_long', 'nextLng')]),
                                lat = as.numeric(points1[i, c('position_lat', 'nextLat')]),
                                color = as.character(points1[i, c('color')]))
  }
  if (alt == TRUE) {
    # for (i in 1:which.max(points1$quantile_alt)) {
    for (i in 1:nrow(points1)) {
     gradient_map <- addPolylines(map = gradient_map,
                                  data = points1,
                                  lng = as.numeric(points1[i, c('position_long', 'nextLng')])-0.005,
                                  lat = as.numeric(points1[i, c('position_lat', 'nextLat')])-0.005,
                                  color = as.character(points1[i, c('color_alt')]))
    }
  }
  gradient_map
}

t <- read.fit("SELECTED_FILES/WhiteLiam/09/WhiteL1994.2019-09-20-23-05-29-293Z.GarminPush.40964805705.fit")
create_map_fit(t,alt=TRUE)

TrainingPeaks Analysis

Extract predictors from activities

We want to calculate different predictors based on the training activity: Phase 1 will contain:

Number of Activities
Volume (Time)
Intensity
- %HRmax
- Thresholds (Time in T1/T2)
- Volume x %HRmax
Sessions per week
Average time
Average speed (cycling vs running, etc?)
- Event type = 1 == cycling??
- sport = 2?
  - 2 = cycling
  - 5 = swimming
- https://developer.garmin.com/downloads/connect-iq/monkey-c/doc/Toybox/ActivityRecording.html
Average HR
Heart beats per week/year of training
Average accumulated power
Data from TrainingPeaks? (data$session)
- Calories
- Total ascent
- max_power
- average_power / normalized_power
- total work
- training_stress_score
- total_distance
- intensity_factor
- avg_HR

Predictors

Read activity data (Only 1 for the moment, but will loop through all activities later):

# data <- read.fit('fit-test_cycling_2019-04-16_05-29-42.csv.fit')
# data <- read.fit('RAW/WorkoutFileExport-White-Liam-2019-04-01-2019-10-01/WhiteL1994.2019-04-16-07-58-54-572Z.GarminPush.33102899171.fit')
data <- read.fit('RAW/WorkoutFileExport-White-Liam-2019-04-01-2019-10-01/WhiteL1994.2019-04-17-08-34-41-343Z.GarminPush.33150696869.fit')
baddata <- read.fit("SELECTED_FILES/WhiteLiam/09/WhiteL1994.2019-09-12-07-01-07-759Z.GarminPush.40489308023.fit")

Initialize predictors df with activity id:

Number of Activities

predictors <- as.data.frame(matrix(1,ncol=1))
colnames(predictors) <- c("activity.id")

Volume (Time)

activity.time <- round((max(data$record$timestamp)-data$record$timestamp[1])/60,2)
predictors$time.min <- activity.time

Intensity
- %HRmax
- Volume x %HRmax

predictors$hrmax.athlete <- data$zones_target$max_heart_rate
predictors$hrmax.activity <- max(data$record$heart_rate)
predictors$hrmax.perc <- round(predictors$hrmax.activity/predictors$hrmax.athlete*100,2)
predictors$hrmax.intensity <- predictors$hrmax.perc * predictors$time.min

Thresholds (Time in Z5/Z4/Z3) https://blogs.sas.com/content/efs/2018/01/26/data-driven-fitness-vo2-max-lactate-threshold-heart-rate/

hr.zones <- quantile(c(1:predictors$hrmax.athlete),probs=seq(0,1,by=0.1))
# we want zones 1 to 5
data$record$hr.zones <- findInterval(data$record$heart_rate,hr.zones[6:10])
hr.zones.table <- round(table(data$record$hr.zones)/sum(table(data$record$hr.zones))*100,1)
predictors$hr.z5 <- hr.zones.table[6]
predictors$hr.z4 <- hr.zones.table[5]
predictors$hr.z3 <- hr.zones.table[4]
predictors$hr.z2 <- hr.zones.table[3]
predictors$hr.z1 <- hr.zones.table[2]

Sessions per week (ATHLETE CALCULATION) Will add a week counter

predictors$week <- 1

Average HR

# predictors$hr.avg <- mean(data$record$heart_rate)
predictors$hr.avg <- data$session$avg_heart_rate

Average speed (cycling vs running, etc?)
- Event type = 1 == cycling??
- sport = 2?

sport_code <- c(0,1,2,5,10,15)
sport_type <- c("Undefined","Running","Cycling","Swimming","Rowing")
predictors$sport_code <- data$session$sport
predictors$sport_type <- sport_type[which (sport_code == predictors$sport_code)]
predictors$speed.avg <- round(data$session$avg_speed*3.79,1)

Average accumulated power (ATHLETE CALCULATION)
Heart beats per week/year of training (ATHLETE CALCULATION)
Data from TrainingPeaks? (data$session)
- Calories

predictors$cal <- data$session$total_calories

Total ascent

predictors$ascent <- data$session$total_ascent

max_power
average_power / normalized_power

predictors$power.max <- data$session$max_power
predictors$power.avg <- data$session$avg_power
predictors$power.norm <- data$session$normalized_power

total work

predictors$work <- data$session$total_work

training_stress_score

predictors$stress.score <- data$session$training_stress_score

total_distance

predictors$total.dist <- data$session$total_distance

intensity_factor

predictors$intensity.factor <- data$session$intensity_factor

Which will give us a table as follows:

predictors

Subset 10 athletes

Athlete original data

We will have VO2max for all participants (Name, VO2max, Weight):

library(purrr)
vo2max <- read.csv("VO2maxdata.csv")
vo2max <- vo2max[vo2max$EventName == "Baseline",]
row.names(vo2max) <- 1:nrow(vo2max)
vo2max <- vo2max %>% mutate(name= map(strsplit(as.character(Athlete), "(?!^)(?=[[:upper:]])", perl=T),~.x[1]) %>% unlist())
vo2max

Athlete input files

Get all files for the 10 athletes (September 2019):

dirs <- list.dirs("SELECTED_FILES",recursive=F)
for (ath in vo2max$name){
  k <- paste0(dirs[grep(tolower(ath),tolower(dirs))],"/09")
  print(k)
  print(head(list.files(k),1))
}

[1] "SELECTED_FILES/WhiteLiam/09"
[1] "WhiteL1994.2019-09-01-03-14-36-346Z.GarminPush.39879313546.fit"
[1] "SELECTED_FILES/MichelmoreKatie/09"
[1] "katiemichelmore.2019-09-04-00-33-02-272Z.GarminPush.40039260489.fit"
[1] "SELECTED_FILES/WhiteNicholas/09"
[1] "nickwhitey97.2019-09-01-13-48-28-116Z.GarminPush.39904430313.fit"
[1] "SELECTED_FILES/JennerSamuel/09"
[1] "2019-09-01-095353-ELEMNT BOLT 6A50-69-0.fit"
[1] "SELECTED_FILES/LoneraganBryn/09"
[1] "bloneragan.2019-09-01-05-32-27-608Z.GarminPush.39882875786.fit"
[1] "SELECTED_FILES/WillOckenden/09"
[1] "2019-09-03-191932-ELEMNT BOLT 8AFB-140-0.fit"
[1] "SELECTED_FILES/PaolilliDomenic/09"
[1] "DomenicPaolilli.2019-09-04-05-08-40-874Z.GarminPush.40048137906.fit"
[1] "SELECTED_FILES/Veriskirstydeacon/09"
[1] "kirstydeacon.2019-09-01-03-46-50-971Z.GarminPush.39879951239.fit"
[1] "SELECTED_FILES/AlbrechtJasper/09"
[1] "ALBRECHT1.2019-09-01-09-40-41-058Z.GarminPush.39893460390.fit"
[1] "SELECTED_FILES/McKennaDylan/09"
[1] "dylanmckenna98.2019-09-01-05-07-55-531Z.GarminPush.39882056069.fit"

Predictors for all activities

Our goal now is to create a framework that inputs a list of files and returns a table with all predictors. First for all Fit files of athletes and then, calculate totals and averages for each of the athletes.

Test Case (WhiteLiam)

Get all WhiteLiam files

whitefiles <- paste0(paste0(dirs[grep("liam",tolower(dirs))],"/09/"),list.files(paste0(dirs[grep("liam",tolower(dirs))],"/09")))

And create a function to calculate all predictors as previously, given a list of Fit files:

calc_predictors <- function (fitFiles) {
  #initialize predictors
  all.predictors <- as.data.frame(matrix(vector(),ncol=25))
  colnames(all.predictors) <- c("activity.id","time.min","hrmax.athlete","hrmax.activity",
                            "hrmax.perc","hrmax.intensity","hr.z5","hr.z4","hr.z3",
                            "hr.z2","hr.z1","week","hr.avg","sport_code","sport_type","speed.avg","cal",
                            "ascent","power.max","power.avg","power.norm","work",
                            "stress.score","total.dist","intensity.factor")
  activity.id = 1
  for (myfile in fitFiles) {
     # print(myfile)
     data <- read.fit(myfile)
     #check if there are NAs in data$record (some cases seen) --> JennerSamuel [5]
     # if t
     predictors <- as.data.frame(matrix(NA,ncol=25))
     colnames(predictors) <- c("activity.id","time.min","hrmax.athlete","hrmax.activity",
                            "hrmax.perc","hrmax.intensity","hr.z5","hr.z4","hr.z3",
                            "hr.z2","hr.z1","week","hr.avg","sport_code","sport_type",
                            "speed.avg","cal",
                            "ascent","power.max","power.avg","power.norm","work",
                            "stress.score","total.dist","intensity.factor")
     activity.time <- round((max(data$record$timestamp)-data$record$timestamp[1])/60,2)
     predictors$time.min <- activity.time
     predictors$activity.id <- activity.id
     #check if there is heart_rate info (not in swimming, surfing and other watersports)
     if(is.null(data$zones_target$max_heart_rate)){
       predictors$hrmax.athlete <- 200
     } else {
       predictors$hrmax.athlete <- data$zones_target$max_heart_rate
     }
     if (any(names(data$record) %in% "heart_rate")){
       predictors$hrmax.activity <- max(data$record$heart_rate
                                        [1:(length(data$record$heart_rate)-3)],na.rm=T)
       predictors$hrmax.perc <- round(predictors$hrmax.activity/predictors$hrmax.athlete*100,2)
       predictors$hrmax.intensity <- predictors$hrmax.perc * predictors$time.min
       hr.zones <- quantile(c(1:predictors$hrmax.athlete),probs=seq(0,1,by=0.1))
       data$record$hr.zones <- findInterval(data$record$heart_rate,hr.zones[6:10])
       hr.zones.table<-round(table(data$record$hr.zones)/sum(table(data$record$hr.zones))*100,1)
       predictors$hr.z5 <- hr.zones.table[6]
       predictors$hr.z4 <- hr.zones.table[5]
       predictors$hr.z3 <- hr.zones.table[4]
       predictors$hr.z2 <- hr.zones.table[3]
       predictors$hr.z1 <- hr.zones.table[2]
       predictors$hr.avg <- data$session$avg_heart_rate
     } else {
       predictors$hrmax.athlete <- NA
       predictors$hrmax.activity <- NA
       predictors$hrmax.perc <- NA
       predictors$hrmax.intensity <- NA
       predictors$hr.z5 <- NA
       predictors$hr.z4 <- NA
       predictors$hr.z3 <- NA
       predictors$hr.z2 <- NA
       predictors$hr.z1 <- NA
       predictors$hr.avg <- NA
     }
     predictors$week <- 1
     predictors$sport_code <- data$session$sport
     predictors$sport_type <- sport_type[which (sport_code == predictors$sport_code)]
     predictors$speed.avg <- round(data$session$avg_speed*3.79,1)
     predictors$cal <- if(is.null(data$session$total_calories)){NA}else{data$session$total_calories}
     predictors$ascent <- if(is.null(data$session$total_ascent)){NA}else{data$session$total_ascent}
     predictors$power.max <- if(is.null(data$session$max_power)){NA}else{data$session$max_power}
     predictors$power.avg <- if(is.null(data$session$avg_power)){NA}else{data$session$avg_power}
     predictors$power.norm <- if(is.null(data$session$normalized_power)){NA}else{data$session$normalized_power}
     predictors$work <- if(is.null(data$session$total_work)){NA}else{data$session$total_work}
     predictors$stress.score <- if(is.null(data$session$training_stress_score)){NA}
                                else {data$session$training_stress_score}
     predictors$total.dist <- data$session$total_distance
     predictors$intensity.factor <-  if(is.null(data$session$intensity_factor)){NA}
                                     else{data$session$intensity_factor}
     all.predictors <- rbind(all.predictors,predictors)
     activity.id <- activity.id+1
     # print(all.predictors)
  }
   return(all.predictors)
}

Example Output

The function created above gives us the following results for the test with WhiteLiam files:

athlete.predictors <- calc_predictors(whitefiles)
athlete.predictors

Superpredictors as summary

Superpredictors are averages or totals of each of the activity predictors, as a way to summarize training data for each athlete during the selected period. Here we calculate all of them to compare with vo2max in the final step.

Definition

Define function to: * Initialize variables

# final.data <- vo2max
prep_superpredictors <- function(final.data){
  final.data$activities.total <- NA
  final.data$time.total <- NA
  final.data$time.avg <- NA
  final.data$hrmax.perc.avg <- NA
  final.data$hrmax.max <- NA
  final.data$hrmax.intensity.total <- NA
  final.data$hrmax.intensity.avg <- NA
  final.data$hr.z5.avg <- NA
  final.data$hr.z4.avg <- NA
  final.data$hr.z3.avg <- NA
  final.data$hr.z2.avg <- NA
  final.data$hr.z1.avg <- NA
  final.data$hr.avg <- NA
  final.data$cal.total <- NA
  final.data$cal.avg <- NA
  final.data$power.avg <- NA
  final.data$work.avg <- NA
  final.data$stress.score.avg <- NA
  final.data$dist.total <- NA
  final.data$dist.avg <- NA
  final.data$intensity.factor.avg <- NA
  return(final.data)
}

Calculate variables

# ath <- 'Liam'
get_superpredictors <- function (ath.data,ath.predictors,ath.name) {
  ath.data[ath.data$name == ath.name,]$activities.total <- nrow(ath.predictors)
  ath.data[ath.data$name == ath.name,]$time.total <- sum(ath.predictors$time.min)
  ath.data[ath.data$name == ath.name,]$time.avg <- mean(ath.predictors$time.min,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hrmax.perc.avg <- mean(ath.predictors$hrmax.perc,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hrmax.max <- if (sum(!is.na(ath.predictors$hrmax.activity)) == 0 ){NA}else{max(ath.predictors$hrmax.activity,na.rm=T)}
  ath.data[ath.data$name == ath.name,]$hrmax.intensity.total <- sum(ath.predictors$hrmax.intensity,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hrmax.intensity.avg <- mean(ath.predictors$hrmax.intensity,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.z5.avg <- mean(ath.predictors$hr.z5,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.z4.avg <- mean(ath.predictors$hr.z4,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.z3.avg <- mean(ath.predictors$hr.z3,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.z2.avg <- mean(ath.predictors$hr.z2,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.z1.avg <- mean(ath.predictors$hr.z1,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.avg <- mean(ath.predictors$hr.avg,na.rm=T)
  ath.data[ath.data$name == ath.name,]$cal.total <- sum(ath.predictors$cal,na.rm=T)
  ath.data[ath.data$name == ath.name,]$cal.avg <- mean(ath.predictors$cal,na.rm=T)
  ath.data[ath.data$name == ath.name,]$power.avg <- mean(ath.predictors$power.avg,na.rm=T)
  ath.data[ath.data$name == ath.name,]$work.avg <- mean(ath.predictors$work,na.rm=T)
  ath.data[ath.data$name == ath.name,]$stress.score.avg <- mean(ath.predictors$stress.score,na.rm=T)
  ath.data[ath.data$name == ath.name,]$dist.total <- sum(ath.predictors$total.dist,na.rm=T)
  ath.data[ath.data$name == ath.name,]$dist.avg <- mean(ath.predictors$total.dist,na.rm=T)
  ath.data[ath.data$name == ath.name,]$intensity.factor.avg <- mean(ath.predictors$intensity.factor,na.rm=T)
  ath.data[is.na(ath.data)] <- NA
  return(ath.data)
}

Calculate Superpredictors

Combine the reading of the files with the superpredictor calculation

dirs <- list.dirs("SELECTED_FILES",recursive=F)
ath.superpreds <- prep_superpredictors(vo2max)

for (ath in vo2max$name){
# for (ath in 'Liam'){
  ath.folder <- paste0(dirs[grep(tolower(ath),tolower(dirs))],"/09/")
  myfiles <- paste0(ath.folder, list.files(paste0(ath.folder)))
  #get predictors
  ath.predictors <- calc_predictors(myfiles)
  #get superpredictors
  ath.superpreds <- get_superpredictors(ath.superpreds,ath.predictors,ath)
}

And the final results for all the athletes:

ath.superpreds

Make correlation with VO2max

Correlate all superpredictors with VO2max. Each point corresponds to an athlete:

models <- as.data.frame(matrix(NA,ncol=22))
models <- rbind(models,NA)
row.names(models) <- c("rsquare","pvalue")
colnames(models) <- names(ath.superpreds)[c(5,7:dim(ath.superpreds)[2])]

for (mod in names(models)){
  md <- paste("VO2max ~ ",mod,sep = "")
  lmTemp = lm(md, data = ath.superpreds) #Create the linear regression
  plot(ath.superpreds[[mod]],ath.superpreds$VO2max, pch = 16, col = "blue",xlab=mod,ylab="VO2max") #Plot the results
  abline(lmTemp) #Add a regression line
  # print(summary(lmTemp))
  models[[mod]] <- c(summary(lmTemp)$adj.r.squared,summary(lmTemp)$coefficients[2,4])
}

models

Model with all variables

md <- paste("VO2max ~ ",paste(names(models)[1:3],collapse="+"),sep = "")
lmTemp = lm(md, data = ath.superpreds) #Create the linear regression
plot(ath.superpreds[[mod]],ath.superpreds$VO2max, pch = 16, col = "blue",xlab=mod,ylab="VO2max") #Plot the results
abline(lmTemp) #Add a regression line

only using the first two of 4 regression coefficients

# print(summary(lmTemp))
models$ALL <- c(summary(lmTemp)$adj.r.squared,summary(lmTemp)$coefficients[2,4])
summary(lmTemp)


Call:
lm(formula = md, data = ath.superpreds)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.56419 -0.24564  0.02118  0.28234  0.40622 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)  
(Intercept)       4.4153795  2.2613275   1.953   0.0987 .
Weight           -0.0052206  0.0382897  -0.136   0.8960  
activities.total -0.0181568  0.0339628  -0.535   0.6121  
time.total        0.0001865  0.0001143   1.631   0.1540  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4167 on 6 degrees of freedom
Multiple R-squared:  0.4025,    Adjusted R-squared:  0.1037 
F-statistic: 1.347 on 3 and 6 DF,  p-value: 0.3449

models

---
title: "Training Peaks"
output:
  html_notebook:
    toc: true
    toc_depth: 3
    toc_float:
      collapsed: false
      smooth_scroll: true
    theme: cosmo
    df_print: paged
    highlight: tango
    code_folding: hide
    # fig_width: 12
    # fig_height: 12
# output:
#   epuRate::BAKER:
#     toc: TRUE
#     number_sections: FALSE
#     code_folding: "show"
---

<script>
$(document).ready(function() {
  $items = $('div#TOC li');
  $items.each(function(idx) {
    num_ul = $(this).parentsUntil('#TOC').length;
    $(this).css({'text-indent': num_ul * 10, 'padding-left': 0});
  });

});
</script>

***

# TrainingPeaks Data

## Preliminary tasks
### Load Libraries

Load libraries for reading fit data and create maps and plots.

```{r warning=FALSE, results='hide', echo='FALSE'}
library(fit)
library(ggplot2)
library(leaflet)
library(dplyr)
library(cetcolor)
```

*** 
## Manual Example (WhiteLiam)

Date: 16 April 2019, cycling

File: fit-test_cycling_2019-04-16_05-29-42.csv.fit

```{r}
data <- read.fit('fit-test_cycling_2019-04-16_05-29-42.csv.fit')
```

### What can we get from TrainingPeaks?

Lots of metadata from the fit file:
*  file id
*  device settings
*  user properties
*  sport / sport type
* etc. 

```{r echo = False}
names(data)
```

And more specifics about the activity:

```{r echo = False}
head(data$record)
```

With their corresponding units:

```{r}
matrix(c(attr(data$record,'names'),attr(data$record,'units')),ncol=2)
```

### Basic Plots

#### Elevation vs Time

```{r fig.width = 6}
pdata <- with(data$record, data.frame(alt = altitude, time = (timestamp-timestamp[1])/60))
 ggplot(pdata, aes(y=alt, x=time)) + geom_line() +
   ggtitle("Elevation vs Time") + xlab("time (minutes)") + ylab("elevation (m)")
```

#### Elevation vs Distance

```{r fig.width = 6}
pdata <- with(data$record, data.frame(alt = altitude, time = (distance-distance[1])/1000))
 ggplot(pdata, aes(y=alt, x=time)) + geom_line() +
   ggtitle("Elevation vs Distance") + xlab("Distance (km)") + ylab("elevation (m)")
```

#### Summary of all variables (Strava-like)

```{r fig.width = 6}
pdata <- with(data$record, data.frame(alt = altitude, time = (distance-distance[1])/1000,
                                      pow = power, hr=heart_rate, speed=speed, cad=cadence))
pdata <- pdata[-(1:10),]
pdata$grad <- with(data$record, 100 * diff(altitude,lag=10) / diff(distance,lag=10))

 ggplot(pdata, aes(y=alt, x=time)) +
   geom_line(aes(y=alt,colour="Altitude")) +
   geom_line(aes(y=pow*2,colour="Power"),size=0.05) +
   geom_line(aes(y=hr*2,colour="HR"),size=0.1) +
   geom_line(aes(y=speed*3.69*2,colour="Speed"),size=0.1) +
   geom_line(aes(y=cad*2,colour="Cadence"),size=0.05) +
   geom_line(aes(y=grad*10+min(pdata$alt),colour="Gradient"),size=0.2) +
   # geom_abline(intercept=min(pdata$alt), slope=0,color="orange",size=1,lty="longdash") +
   geom_segment(aes(x = 0, xend = max(pdata$time), y = min(pdata$alt), yend = min(pdata$alt)),color="orange",size=0.8) +
   scale_y_continuous(sec.axis = sec_axis(~./2, name = "HR (bpm) / Power (W) / Gradient (%) \n Speed (kph) / Cadence(rpm)"),
                      breaks = seq(0, 1000, by = 200)) +
   scale_x_continuous(breaks= seq(0, 70, by = 10)) +
   ggtitle("Distance vs All") +
   xlab("Distance (km)") +
   ylab("Altitude (m)") +
   scale_colour_manual(values = c("black", "purple","orange","red","blue","green")) +
   labs(y = "Altitude (m)",
                x = "Distance (km)",
                colour = "") +
   theme_minimal() +
   # theme(legend.position = c(0.9, 0.93))
   theme(legend.position = "bottom")
```

#### Heart Rate vs Gradient

The steepest the climb, the hardest the heart has to work.

```{r fig.width = 6}
pdata <- data$record[-(1:10),c("heart_rate","timestamp")]
# compute average gradient, as %
 pdata$gradient <- with(data$record, 100 * diff(altitude,lag=10) / diff(distance,lag=10))
 pdata <- subset(pdata, complete.cases(pdata) & abs(gradient) < 7.5 & gradient != 0) # drop outliers
 ggplot(pdata, aes(x=gradient, y=heart_rate)) +
   geom_point(alpha=0.5) + geom_jitter() +
   stat_smooth(method="lm", formula=y ~ poly(x, 2)) +
   ggtitle("Heart rate vs gradient")
```

With the corresponding correlation:

```{r echo =F}
 fit <- lm(heart_rate ~ poly(gradient, 2), data=pdata)
 summary(fit)
```

And averages for each gradient step:

```{r}
pred <- data.frame(gradient = seq(-6,6,2))
pred$heart_rate <- predict(fit, pred)
t(pred)
```

### Activity Maps

Convert time to minutes and add direction (Outbound vs Return):
 
```{r}
 # points <- subset(data$record, complete.cases(data$record))
points <- data$record
points$time_min  <- with(points, timestamp - timestamp[1])/60 # minutes of riding

# from diagram above, we turned around at the 90 minutes mark
# points[which(points$altitude == max(points$altitude)),]
points$direction <- with(points, factor(ifelse(time_min < 90, 'Outbound', 'Return')))
```

```{r}
 # library(leaflet)
 # leaflet(points[points$direction == 'Outbound',]) %>% addTiles() %>% addPolylines(~position_long,~position_lat)
 # leaflet(points[points$direction == 'Return',]) %>% addTiles() %>% addPolylines(~position_long,~position_lat)
```

#### Map with HR

The darker the colour, the higher the HR is:

```{r}
newcols <- rev(cet_pal(min(dim(points)[1],256),name="l3"))
points <- points %>% mutate(quantile=ntile(heart_rate,256))
newcols.quantile <- newcols[points$quantile]
points1 <- points %>%
 mutate(nextLat = lead(position_lat),
        nextLng = lead(position_long),
        color = newcols.quantile
        )
gradient_map <- leaflet() %>%
 addTiles()
# points1 <- points1[points1$direction == 'Outbound',]
for (i in 1:nrow(points1)) {
 gradient_map <- addPolylines(map = gradient_map,
                              data = points1,
                              lng = as.numeric(points1[i, c('position_long', 'nextLng')]),
                              lat = as.numeric(points1[i, c('position_lat', 'nextLat')]),
                              color = as.character(points1[i, c('color')])
 )
}
gradient_map
```

#### Map with HR and profile

And we can overlap different measurements to the same plot.
The darker the colour, the higher the HR and the higher the altitude (for the new line):


```{r}
newcols <- rev(cet_pal(min(dim(points)[1],256),name="l3"))
newcols_alt <- rev(cet_pal(min(dim(points)[1],256),name="l7"))
points <- points %>% mutate(quantile=ntile(heart_rate,256))
points <- points %>% mutate(quantile_alt=ntile(altitude,256))
newcols.quantile <- newcols[points$quantile]
points1 <- points %>%
 mutate(nextLat = lead(position_lat),
        nextLng = lead(position_long),
        color = newcols.quantile,
        color_alt = newcols_alt[points$quantile_alt]
        )
gradient_map <- leaflet() %>% addTiles()
# points1 <- points1[points1$direction == 'Outbound',]
for (i in 1:nrow(points1)) {
# for (i in 1:500) {
 gradient_map <- addPolylines(map = gradient_map,
                              data = points1,
                              lng = as.numeric(points1[i, c('position_long', 'nextLng')]),
                              lat = as.numeric(points1[i, c('position_lat', 'nextLat')]),
                              color = as.character(points1[i, c('color')])
 )
}

for (i in 1:which.max(points1$quantile_alt)) {
# for (i in 1:500) {
 gradient_map <- addPolylines(map = gradient_map,
                              data = points1,
                              lng = as.numeric(points1[i, c('position_long', 'nextLng')])-0.005,
                              lat = as.numeric(points1[i, c('position_lat', 'nextLat')])-0.005,
                              color = as.character(points1[i, c('color_alt')])
 )
}

gradient_map
```

#### Map of a different activity

Function to create map:

```{r}
create_map_fit <- function (fit_data,alt){
  points <- fit_data$record
  newcols <- rev(cet_pal(min(dim(points)[1],256),name="l3"))
  newcols_alt <- rev(cet_pal(min(dim(points)[1],256),name="l7"))
  points <- points %>% mutate(quantile=ntile(heart_rate,256))
  if (alt == TRUE) {
    points <- points %>% mutate(quantile_alt=ntile(altitude,256))
  }
  if (alt == TRUE) {
    points1 <- points %>% mutate(nextLat = lead(position_lat),
                               nextLng = lead(position_long),
                               color = newcols[points$quantile],
                               color_alt = newcols_alt[points$quantile_alt])
  } else {
    points1 <- points %>% mutate(nextLat = lead(position_lat),
                               nextLng = lead(position_long),
                               color = newcols[points$quantile])
  }
  
  #prepare map
  gradient_map <- leaflet() %>% addTiles()
  for (i in 1:nrow(points1)) {
   gradient_map <- addPolylines(map = gradient_map,
                                data = points1,
                                lng = as.numeric(points1[i, c('position_long', 'nextLng')]),
                                lat = as.numeric(points1[i, c('position_lat', 'nextLat')]),
                                color = as.character(points1[i, c('color')]))
  }
  if (alt == TRUE) {
    # for (i in 1:which.max(points1$quantile_alt)) {
    for (i in 1:nrow(points1)) {
     gradient_map <- addPolylines(map = gradient_map,
                                  data = points1,
                                  lng = as.numeric(points1[i, c('position_long', 'nextLng')])-0.005,
                                  lat = as.numeric(points1[i, c('position_lat', 'nextLat')])-0.005,
                                  color = as.character(points1[i, c('color_alt')]))
    }
  }
  gradient_map
}
```

```{r}
t <- read.fit("SELECTED_FILES/WhiteLiam/09/WhiteL1994.2019-09-20-23-05-29-293Z.GarminPush.40964805705.fit")
create_map_fit(t,alt=TRUE)
```

***

# TrainingPeaks Analysis

## Extract predictors from activities

We want to calculate different predictors based on the training activity:
Phase 1 will contain:
 
* Number of Activities
* Volume (Time)
* Intensity
  * %HRmax
  * Thresholds (Time in T1/T2)
  * Volume x %HRmax
* Sessions per week
* Average time
* Average speed (cycling vs running, etc?)
  * Event type = 1 == cycling??
  * sport = 2?
    * 2 = cycling
    * 5 = swimming
  * https://developer.garmin.com/downloads/connect-iq/monkey-c/doc/Toybox/ActivityRecording.html
* Average HR
* Heart beats per week/year of training
* Average accumulated power
* Data from TrainingPeaks? (data$session)
  * Calories
  * Total ascent
  * max_power
  * average_power / normalized_power
  * total work
  * training_stress_score
  * total_distance
  * intensity_factor
  * avg_HR

### Predictors

Read activity data
(Only 1 for the moment, but will loop through all activities later):

```{r}
# data <- read.fit('fit-test_cycling_2019-04-16_05-29-42.csv.fit')
# data <- read.fit('RAW/WorkoutFileExport-White-Liam-2019-04-01-2019-10-01/WhiteL1994.2019-04-16-07-58-54-572Z.GarminPush.33102899171.fit')
data <- read.fit('RAW/WorkoutFileExport-White-Liam-2019-04-01-2019-10-01/WhiteL1994.2019-04-17-08-34-41-343Z.GarminPush.33150696869.fit')
baddata <- read.fit("SELECTED_FILES/WhiteLiam/09/WhiteL1994.2019-09-12-07-01-07-759Z.GarminPush.40489308023.fit")
```

Initialize predictors df with activity id:

*  Number of Activities

```{r}
predictors <- as.data.frame(matrix(1,ncol=1))
colnames(predictors) <- c("activity.id")
```

* Volume (Time)

```{r}
activity.time <- round((max(data$record$timestamp)-data$record$timestamp[1])/60,2)
predictors$time.min <- activity.time
```

* Intensity
  * %HRmax
  * Volume x %HRmax
  
```{r}
predictors$hrmax.athlete <- data$zones_target$max_heart_rate
predictors$hrmax.activity <- max(data$record$heart_rate)
predictors$hrmax.perc <- round(predictors$hrmax.activity/predictors$hrmax.athlete*100,2)
predictors$hrmax.intensity <- predictors$hrmax.perc * predictors$time.min
```
  
  * Thresholds (Time in Z5/Z4/Z3)
https://blogs.sas.com/content/efs/2018/01/26/data-driven-fitness-vo2-max-lactate-threshold-heart-rate/

```{r}
hr.zones <- quantile(c(1:predictors$hrmax.athlete),probs=seq(0,1,by=0.1))
# we want zones 1 to 5
data$record$hr.zones <- findInterval(data$record$heart_rate,hr.zones[6:10])
hr.zones.table <- round(table(data$record$hr.zones)/sum(table(data$record$hr.zones))*100,1)
predictors$hr.z5 <- hr.zones.table[6]
predictors$hr.z4 <- hr.zones.table[5]
predictors$hr.z3 <- hr.zones.table[4]
predictors$hr.z2 <- hr.zones.table[3]
predictors$hr.z1 <- hr.zones.table[2]
```
  
* Sessions per week (ATHLETE CALCULATION)
Will add a week counter

```{r}
predictors$week <- 1
```

* Average HR

```{r}
# predictors$hr.avg <- mean(data$record$heart_rate)
predictors$hr.avg <- data$session$avg_heart_rate
```

* Average speed (cycling vs running, etc?)
  * Event type = 1 == cycling??
  * sport = 2?

```{r}
sport_code <- c(0,1,2,5,10,15)
sport_type <- c("Undefined","Running","Cycling","Swimming","Rowing")
predictors$sport_code <- data$session$sport
predictors$sport_type <- sport_type[which (sport_code == predictors$sport_code)]
predictors$speed.avg <- round(data$session$avg_speed*3.79,1)
```
  
  * Average accumulated power (ATHLETE CALCULATION)
* Heart beats per week/year of training (ATHLETE CALCULATION)
* Data from TrainingPeaks? (data$session)
  * Calories
```{r}
predictors$cal <- data$session$total_calories
```
  * Total ascent
```{r}
predictors$ascent <- data$session$total_ascent
```

  * max_power
  * average_power / normalized_power
  
```{r}
predictors$power.max <- data$session$max_power
predictors$power.avg <- data$session$avg_power
predictors$power.norm <- data$session$normalized_power
```
  * total work
```{r}
predictors$work <- data$session$total_work
```
  * training_stress_score
```{r}
predictors$stress.score <- data$session$training_stress_score
```
  * total_distance
```{r}
predictors$total.dist <- data$session$total_distance
```
  * intensity_factor
```{r}
predictors$intensity.factor <- data$session$intensity_factor
```


Which will give us a table as follows:

```{r}
predictors
```

*** 

## Subset 10 athletes

### Athlete original data

We will have VO2max for all participants (Name, VO2max, Weight):

```{r}
library(purrr)
vo2max <- read.csv("VO2maxdata.csv")
vo2max <- vo2max[vo2max$EventName == "Baseline",]
row.names(vo2max) <- 1:nrow(vo2max)
vo2max <- vo2max %>% mutate(name= map(strsplit(as.character(Athlete), "(?!^)(?=[[:upper:]])", perl=T),~.x[1]) %>% unlist())
vo2max
```

### Athlete input files

Get all files for the 10 athletes (September 2019):

```{r}
dirs <- list.dirs("SELECTED_FILES",recursive=F)
for (ath in vo2max$name){
  k <- paste0(dirs[grep(tolower(ath),tolower(dirs))],"/09")
  print(k)
  print(head(list.files(k),1))
}
```

*** 

## Predictors for all activities

Our goal now is to create a framework that inputs a list of files and returns a table with all predictors.
First for all Fit files of athletes and then, calculate totals and averages for each of the athletes.

### Test Case (WhiteLiam)

Get all WhiteLiam files

```{r}
whitefiles <- paste0(paste0(dirs[grep("liam",tolower(dirs))],"/09/"),list.files(paste0(dirs[grep("liam",tolower(dirs))],"/09")))
```

And create a function to calculate all predictors as previously, given a list of Fit files:

```{r}
calc_predictors <- function (fitFiles) {
  #initialize predictors
  all.predictors <- as.data.frame(matrix(vector(),ncol=25))
  colnames(all.predictors) <- c("activity.id","time.min","hrmax.athlete","hrmax.activity",
                            "hrmax.perc","hrmax.intensity","hr.z5","hr.z4","hr.z3",
                            "hr.z2","hr.z1","week","hr.avg","sport_code","sport_type","speed.avg","cal",
                            "ascent","power.max","power.avg","power.norm","work",
                            "stress.score","total.dist","intensity.factor")
  activity.id = 1
  for (myfile in fitFiles) {
     # print(myfile)
     data <- read.fit(myfile)
     #check if there are NAs in data$record (some cases seen) --> JennerSamuel [5]
     # if t
     predictors <- as.data.frame(matrix(NA,ncol=25))
     colnames(predictors) <- c("activity.id","time.min","hrmax.athlete","hrmax.activity",
                            "hrmax.perc","hrmax.intensity","hr.z5","hr.z4","hr.z3",
                            "hr.z2","hr.z1","week","hr.avg","sport_code","sport_type",
                            "speed.avg","cal",
                            "ascent","power.max","power.avg","power.norm","work",
                            "stress.score","total.dist","intensity.factor")
     activity.time <- round((max(data$record$timestamp)-data$record$timestamp[1])/60,2)
     predictors$time.min <- activity.time
     predictors$activity.id <- activity.id
     #check if there is heart_rate info (not in swimming, surfing and other watersports)
     if(is.null(data$zones_target$max_heart_rate)){
       predictors$hrmax.athlete <- 200
     } else {
       predictors$hrmax.athlete <- data$zones_target$max_heart_rate
     }
     if (any(names(data$record) %in% "heart_rate")){
       predictors$hrmax.activity <- max(data$record$heart_rate
                                        [1:(length(data$record$heart_rate)-3)],na.rm=T)
       predictors$hrmax.perc <- round(predictors$hrmax.activity/predictors$hrmax.athlete*100,2)
       predictors$hrmax.intensity <- predictors$hrmax.perc * predictors$time.min
       hr.zones <- quantile(c(1:predictors$hrmax.athlete),probs=seq(0,1,by=0.1))
       data$record$hr.zones <- findInterval(data$record$heart_rate,hr.zones[6:10])
       hr.zones.table<-round(table(data$record$hr.zones)/sum(table(data$record$hr.zones))*100,1)
       predictors$hr.z5 <- hr.zones.table[6]
       predictors$hr.z4 <- hr.zones.table[5]
       predictors$hr.z3 <- hr.zones.table[4]
       predictors$hr.z2 <- hr.zones.table[3]
       predictors$hr.z1 <- hr.zones.table[2]
       predictors$hr.avg <- data$session$avg_heart_rate
     } else {
       predictors$hrmax.athlete <- NA
       predictors$hrmax.activity <- NA
       predictors$hrmax.perc <- NA
       predictors$hrmax.intensity <- NA
       predictors$hr.z5 <- NA
       predictors$hr.z4 <- NA
       predictors$hr.z3 <- NA
       predictors$hr.z2 <- NA
       predictors$hr.z1 <- NA
       predictors$hr.avg <- NA
     }
     predictors$week <- 1
     predictors$sport_code <- data$session$sport
     predictors$sport_type <- sport_type[which (sport_code == predictors$sport_code)]
     predictors$speed.avg <- round(data$session$avg_speed*3.79,1)
     predictors$cal <- if(is.null(data$session$total_calories)){NA}else{data$session$total_calories}
     predictors$ascent <- if(is.null(data$session$total_ascent)){NA}else{data$session$total_ascent}
     predictors$power.max <- if(is.null(data$session$max_power)){NA}else{data$session$max_power}
     predictors$power.avg <- if(is.null(data$session$avg_power)){NA}else{data$session$avg_power}
     predictors$power.norm <- if(is.null(data$session$normalized_power)){NA}else{data$session$normalized_power}
     predictors$work <- if(is.null(data$session$total_work)){NA}else{data$session$total_work}
     predictors$stress.score <- if(is.null(data$session$training_stress_score)){NA}
                                else {data$session$training_stress_score}
     predictors$total.dist <- data$session$total_distance
     predictors$intensity.factor <-  if(is.null(data$session$intensity_factor)){NA}
                                     else{data$session$intensity_factor}
     all.predictors <- rbind(all.predictors,predictors)
     activity.id <- activity.id+1
     # print(all.predictors)
  }
   return(all.predictors)
}
```

#### Example Output

The function created above gives us the following results for the test with WhiteLiam files:

```{r}
athlete.predictors <- calc_predictors(whitefiles)
athlete.predictors
```

***

## Superpredictors as summary

Superpredictors are averages or totals of each of the activity predictors, as a way to summarize training data for each athlete during the selected period.
Here we calculate all of them to compare with vo2max in the final step.

### Definition

Define function to:
* Initialize variables

```{r}
# final.data <- vo2max
prep_superpredictors <- function(final.data){
  final.data$activities.total <- NA
  final.data$time.total <- NA
  final.data$time.avg <- NA
  final.data$hrmax.perc.avg <- NA
  final.data$hrmax.max <- NA
  final.data$hrmax.intensity.total <- NA
  final.data$hrmax.intensity.avg <- NA
  final.data$hr.z5.avg <- NA
  final.data$hr.z4.avg <- NA
  final.data$hr.z3.avg <- NA
  final.data$hr.z2.avg <- NA
  final.data$hr.z1.avg <- NA
  final.data$hr.avg <- NA
  final.data$cal.total <- NA
  final.data$cal.avg <- NA
  final.data$power.avg <- NA
  final.data$work.avg <- NA
  final.data$stress.score.avg <- NA
  final.data$dist.total <- NA
  final.data$dist.avg <- NA
  final.data$intensity.factor.avg <- NA
  return(final.data)
}
```

* Calculate variables

```{r}
# ath <- 'Liam'
get_superpredictors <- function (ath.data,ath.predictors,ath.name) {
  ath.data[ath.data$name == ath.name,]$activities.total <- nrow(ath.predictors)
  ath.data[ath.data$name == ath.name,]$time.total <- sum(ath.predictors$time.min)
  ath.data[ath.data$name == ath.name,]$time.avg <- mean(ath.predictors$time.min,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hrmax.perc.avg <- mean(ath.predictors$hrmax.perc,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hrmax.max <- if (sum(!is.na(ath.predictors$hrmax.activity)) == 0 ){NA}else{max(ath.predictors$hrmax.activity,na.rm=T)}
  ath.data[ath.data$name == ath.name,]$hrmax.intensity.total <- sum(ath.predictors$hrmax.intensity,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hrmax.intensity.avg <- mean(ath.predictors$hrmax.intensity,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.z5.avg <- mean(ath.predictors$hr.z5,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.z4.avg <- mean(ath.predictors$hr.z4,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.z3.avg <- mean(ath.predictors$hr.z3,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.z2.avg <- mean(ath.predictors$hr.z2,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.z1.avg <- mean(ath.predictors$hr.z1,na.rm=T)
  ath.data[ath.data$name == ath.name,]$hr.avg <- mean(ath.predictors$hr.avg,na.rm=T)
  ath.data[ath.data$name == ath.name,]$cal.total <- sum(ath.predictors$cal,na.rm=T)
  ath.data[ath.data$name == ath.name,]$cal.avg <- mean(ath.predictors$cal,na.rm=T)
  ath.data[ath.data$name == ath.name,]$power.avg <- mean(ath.predictors$power.avg,na.rm=T)
  ath.data[ath.data$name == ath.name,]$work.avg <- mean(ath.predictors$work,na.rm=T)
  ath.data[ath.data$name == ath.name,]$stress.score.avg <- mean(ath.predictors$stress.score,na.rm=T)
  ath.data[ath.data$name == ath.name,]$dist.total <- sum(ath.predictors$total.dist,na.rm=T)
  ath.data[ath.data$name == ath.name,]$dist.avg <- mean(ath.predictors$total.dist,na.rm=T)
  ath.data[ath.data$name == ath.name,]$intensity.factor.avg <- mean(ath.predictors$intensity.factor,na.rm=T)
  ath.data[is.na(ath.data)] <- NA
  return(ath.data)
}
```


### Calculate Superpredictors

Combine the reading of the files with the superpredictor calculation

```{r}
dirs <- list.dirs("SELECTED_FILES",recursive=F)
ath.superpreds <- prep_superpredictors(vo2max)
```


```{r}
for (ath in vo2max$name){
# for (ath in 'Liam'){
  ath.folder <- paste0(dirs[grep(tolower(ath),tolower(dirs))],"/09/")
  myfiles <- paste0(ath.folder, list.files(paste0(ath.folder)))
  #get predictors
  ath.predictors <- calc_predictors(myfiles)
  #get superpredictors
  ath.superpreds <- get_superpredictors(ath.superpreds,ath.predictors,ath)
}
```


And the final results for all the athletes:

```{r}
ath.superpreds
```

***

## Make correlation with VO2max

Correlate all superpredictors with VO2max.
Each point corresponds to an athlete:

```{r}
models <- as.data.frame(matrix(NA,ncol=22))
models <- rbind(models,NA)
row.names(models) <- c("rsquare","pvalue")
colnames(models) <- names(ath.superpreds)[c(5,7:dim(ath.superpreds)[2])]
```


```{r}
for (mod in names(models)){
  md <- paste("VO2max ~ ",mod,sep = "")
  lmTemp = lm(md, data = ath.superpreds) #Create the linear regression
  plot(ath.superpreds[[mod]],ath.superpreds$VO2max, pch = 16, col = "blue",xlab=mod,ylab="VO2max") #Plot the results
  abline(lmTemp) #Add a regression line
  # print(summary(lmTemp))
  models[[mod]] <- c(summary(lmTemp)$adj.r.squared,summary(lmTemp)$coefficients[2,4])
}
```


```{r}
models
```

* Model with all variables

```{r}
md <- paste("VO2max ~ ",paste(names(models)[1:3],collapse="+"),sep = "")
lmTemp = lm(md, data = ath.superpreds) #Create the linear regression
plot(ath.superpreds[[mod]],ath.superpreds$VO2max, pch = 16, col = "blue",xlab=mod,ylab="VO2max") #Plot the results
abline(lmTemp) #Add a regression line
# print(summary(lmTemp))
models$ALL <- c(summary(lmTemp)$adj.r.squared,summary(lmTemp)$coefficients[2,4])
summary(lmTemp)
models
```







Training Peaks